Smart farming technologies adoption : Which factors play a role in the digital transition?

Smart Farming Technologies (SFT) are smart devices part of a cyber-physical system able to improve farm management. Compared to other digital technologies' functionalities, SFT generate a multitude of data that once combined can be used not only on-farm but across the entire supply chain. Although recent studies highlighted how the lack of users’ resources and competences might hinder the diffusion of digital agriculture technologies overall, few studies so far focused specifically on SFT. Moreover, the extant literature interprets the adoption decision mostly as “one-off binary” process, and limited attention is given to individual aspects of users and farms. Therefore, this study investigates the adoption of SFT analyzing various aspects of its complex nature; on the one hand, the analysis considers the multi-step nature of the adoption decision process: first, intention formation and then, actual adoption decision. On the other hand, the SFT adoption process is interpreted as being determined by several typologies of determinants beyond the most studied ones, with a particular focus on the role that organizational conditions and the supply chain governance structure play in influencing farmers' adoption of SFT. An empirical analysis is run on a sample of 474 responses, collected through an on-line survey. Structural Equation Modeling (SEM) and a Zero-Inflated Poisson Regression (ZIP) were used to investigate respectively the intention to use and the actual adoption of SFT. Results show that farmers' intention to use mainly relies on technologies' performance expectancy, technologies' complexity and social influence exerted on farmers, while organizational supporting conditions do not play a significant effect. Nonetheless, when the actual adoption decision is observed, the likelihood that non-adopters intend to adopt SFT in their farms increases when formal integration along the supply chain is high and with the dimension of the farm (in terms of both land size and sales). When the adopters are analyzed instead, the decision to adopt is positively affected only by the individual intention to use and by farmers' specialization in the arable sector. Findings reveal what factors need to be considered to guarantee a fairer and more inclusive agricultural digitalization, such as the role of social influence exerted by some figures around farmers and the still weak facilitating organizational conditions